Graduate Student Researcher

I'm a PhD candidate in Computer Science working with David Jensen in the Knowledge Discovery Laboratory at UMass Amherst and Vikash Mansinghka in the Probabilistic Computing Project at MIT. I study methods that combine expert knowledge of mechanisms with causal machine learning, enabling AI-assisted scientific discovery and explainable AI. In practice, my work focusses on probabilistic programming and Bayesian nonparametric approaches to causal inference with observational, experimental and quasi-experimental data. When I'm not reading, writing, or talking about models, you're likely to find me lost in the woods with my puppy dog Mira.


Assessing Inference Quality for Probabilistic Programs using Multivariate Simulation Based Calibration
Sharan Yalburgi, Cameron Freer, Jameson Quinn, Veronica Weiner, Sam Witty, Vikash Mansinghka (2021).
Third Conference on Probabilistic Progamming. [bibtex]

Causal Probabilistic Programming without Tears
Eli Bingham*, James Koppel*, Alexander Lew*, Robert Ness*, Zenna Tavares*, Sam Witty*, Jeremy Zucker* (2021, Alphabetical Order).
Third Conference on Probabilistic Progamming. [bibtex]

A Simulation-Based Test of Identifiability for Bayesian Causal Inference
Sam Witty, David Jensen, Vikash Mansinghka (2021).
arXiv preprint arXiv:2102.11761 [bibtex]. (Working paper)

Fairkit, Fairkit, on the Wall, Who’s the Fairest of Them All? Supporting Data Scientists in Training Fair Models
Brittany Johnson, Jesse Bartola, Rico Angell, Katherine Keith, Sam Witty, Stephen Giguere, Yuriy Brun (2020).
arXiv preprint arXiv:2012.09951 [bibtex]

Causal Inference using Gaussian Processes with Structured Latent Confounders
Sam Witty, Kenta Takatsu, David Jensen, Vikash Mansinghka (2020).
International Conference on Machine Learning. [bibtex]

Bayesian Causal Inference via Probabilistic Program Synthesis
Sam Witty*, Alexander Lew*, David Jensen, Vikash Mansinghka (2020).
Second Conference on Probabilistic Programming [bibtex]

Measuring and Characterizing Generalization in Deep Reinforcement Learning
Sam Witty, Jun Ki Lee, Emma Tosch, Akanksha Atrey, Michael Littman, David Jensen (2018).
arXiv preprint arXiv:1812.02868 [bibtex]
(Short Version Published at the NeurIPS CRACT Workshop.)

Causal Graphs vs. Causal Programs: The Case of Conditional Branching.
Sam Witty, David Jensen (2018).
First Conference on Probabilistic Programming. [bibtex]

Belief-Space Planning for Automated Malware Defense.
Justin Svegliato, Sam Witty, Amir Houmansadr, Shlomo Zilberstein (2018).
IJCAI Workshop on AI for Internet of Things. [bibtex]


Teaching and Mentorship

  • I was the teaching assistant for CS348, Umass' upper-level undergraduate course on data science. (Spring, 2019)

  • I gave a guest lecture on deep learning for CS589, Umass' Masters course on machine learning. (February 15, 2018)

  • I mentored Catherine Chen, a visiting undergraduate researcher sponsored by the NSF's REU program. (Summer, 2017)